Case study GetYourGuide
Why are so many visitors leaving without buying a ticket?
GetYourGuide told me many visitors were looking for tickets for popular POIs (aka points of interest... think Eiffel Tower in Paris).
And although many visitors landed on the appropriate page, far too many were leaving the page without buying.
My task was to find out what was wrong, and suggest a fix for the problem.
Challenge
- Tourists struggled to find a suitable activity
- Info density too high, especially for mobile screens
- Filtering accessible but unusable for users
Analysis
- Info presentation unsuitable for end customers
- Endlessly long lists causing fatigue
- Sections of information on activites were unclear & general lack of transparency
Solution
- Focus on mobile use case
- Reduction of elements on all screens
- Clear labelling to increase transparency
- Filters used by system to automatically generated "curated" lists
Role
- UX Designer
- Researcher
- + Interviews
- + Card Sorting
- + Usability Tests
Tools
- Paper & Pen
- Sketch
- InVision
Deliverables
- Wireframes
- Layouts
- Prototypes
Screens showing the automatically "curated" list (aka curated by the system), with the old version on the left, and the new version on the right.
The new version utilised the limited real estate on a mobile device much more effectively.
Exploring the problem revealed that GetYourGuide's SEM was quite sophisticated and they bid cleverly on a PPC basis on specific search terms on Google (e.g. "things to do in Paris" or "tickets for the Eiffel Tower").
Those tourists were looking for things to do on holiday, and were considered to be far along their purchase decision (jargon: they demonstrated high intent).
Although tens of thousands of visitors visited the website each and every day, the bounce rate was way too high at over 54%.
Everything was tracked that could be tracked, but metrics alone were not helpful. Simply put, even though frequent a/b tests were run, no one knew why so many visitors were leaving.
Data-driven is not the same as evidence-driven and although large amounts of click data are important, clicks seldom reveal the why of users' actions.
So why were so many visitors leaving?
Testing with users revealed the cause of the high bounce rate: it was the listing of all activities on a very long page (duh!).
GetYourGuide's algorithm highlighted one activity at the top of the long page with a "Top Pick" label.
Testing made it clear that no visitors knew why GetYourGuide's "Top Pick" was indeed top, and this was exasperated by the fact that it didn't look any different to all the other activities on the page.
As a consequence of the lack of transparency, everyone I interviewed felt they had to look at every single possible activity on that page (phew!).
Scrolling up and down a long page full of similar looking items requires a great deal of mental effort (jargon: cognitive load) and is hard work for all users, irrespective of their age or savviness with technology & screens.
The key words here are "similar looking".
Where too many things are similar looking, there's a natural tendency to blend all of them into one big blur.
Further research, interviewing and tests revealed a consistent need & desire and even a mental model, amongst almost all participants.
And a desire to have some kind of pre-selection performed for them... which was really just another way of saying "it's too hard to find something".
Having performed more-or-less all of the research, I was in an advantageous position to suggest a solution.
My first idea was to make the filters readily available. That was an attempt to address the "it's too hard to find something" problem. A quick wireframe/mock-up demonstrated how that minimal real estate on a phone could be used, letting potential customers dive into their preferred type of activity. And a similar exploration for a bigger screen (tablet or desktop).
First responses were positive, allowing visitors to easily get to what was interesting for them. However, that initial positive response turned quickly sour, as visitors were presented with all of their potentially interesting activities on a very long page (which was the cause of the original high bounce rate!).
This led to to, what is in essence, a simple solution: Applying the filters for the visitors, and presenting a dynamic list showing the "Top Pick" from each of the filters.
This entire list would be permanently adapted to show the current "Top Pick" from each filter, and the filters would be adapted to seasonal fluctuations, as measured by the analysts.
This is how I came up with an automated curated list.